Giordano Dance Chicago, Chicago, IL. Photo by Gorman Cook Photography. Giordano Dance Chicago, Chicago, IL. Photo by Gorman Cook Photography.
In this update, we reexamine the impact of COVID on performing arts ticket sales for 100 organizations, using purchase data for January 1, 2018-December 31, 2021. The analysis updates two earlier sets of results, one of which examined ticket sales data through June 30, 2021 and another that examined ticket sales data through September 30, 2021. At that time, we estimated the total losses in the nonprofit performing arts industry attributable to the pandemic through December 2021 likely exceed $3.2B. That estimate is unchanged by the current results. We also concluded that lagging vaccination rates cost this industry around $10M per month for every unrealized percentage point in vaccination rates. That result no longer holds, as waning vaccine efficacy and soaring infection rates and breakthrough cases have shifted demand patterns among the vaccinated.
Our takeaways from the latest set of results can be summarized as:
These takeaways emerge from our analysis of ticket purchases at 100 performing arts organizations across the country, which ranged from under $1 million to $300+ million in total annual expenses pre-COVID (see Model Details and Limitations section below for additional information). Figure 1 plots historical values that are normalized on a scale of 0-100 for the characteristics, or “variables,” that vary by month. This way, you can see when each variable was at its maximum (100%) over the period as well as its level of volatility.
Trends indicate that ticket purchases pre-COVID were lower in summer months and January. After the arrival of COVID, ticket purchases plummeted to about 5% of their peak level in August and September 2020, whereas traffic counts bottomed out in April 2020 at roughly 32% of their peak level. Vaccination rates started climbing and COVID case rates declined in February 2021; in response, household ticket purchases, average ticket prices, and the number of performances offered rebounded, as did traffic counts. But then COVID cases shot up in August and September 2021, and ticket sales dropped. COVID cases dropped again in October and November 2021 and ticket sales rose. Omicron hit in December 2021 and ticket sales dropped again.
In December, we reported two shifts in ticket demand in response to the evolving COVID environment. Demand was becoming less responsive over time to vaccination rates and more responsive to COVID cases. As we explained in December, diminishing effects for vaccination rates are expected as rates begin to approach 100%.
Figure 2 shows the results of three sets of analyses, focusing on the effect of vaccination rates. The June 2021 results replicate our previous analysis using data from January 2018-June 2021 (see Model Details and Limitations section below). In June 2021, the average vaccination rate was just over 40% and most counties were between 20% and 60%. The June analysis uncovered a positive, linear relationship between vaccination rates and expected ticket purchases per household census tract (HHCT) per month. Households in counties with higher vaccination rates purchased more tickets to the performing arts.
The September 2021 results replicate our previous analysis that used all data from January 2018-September 2021. In September 2021, the average vaccination rate was just over 50% and most counties were between 30 and 70%. This analysis indicates a positive curvilinear relationship between vaccination rates and ticket purchases. The curve for vaccination rates between 10% and 50% is nearly the same in the September analysis as it was in the June analysis. But the curve between 50% and 80% in the September analysis is much flatter, indicating diminishing positive effects.
The December 2021 results use all data from January 2018-December 2021. In December 2021, the average vaccination rate was just over 60%, and most counties were between 40 and 80%. In this analysis, the curve for vaccination rates between 10% and 50% is similar to the September curve. But the curve between 50% and 80% turns negative. Counties with very high vaccination rates (e.g., 80%) are less likely to buy tickets than counties with average vaccination rates (e.g., 60%).
The data clearly show how the relationship between COVID vaccination rates and ticket purchase has evolved over time. Our interpretation is as follows. Early on, the messaging around vaccinations was, “Get vaccinated, avoid COVID, and resume a normal life.” (See, for example, "CDC Says Vaccinated People Can Go Back to Normal Life") Early adopters embraced this message, got vaccinated, and began buying tickets to the performing arts.
With the emergence of the more virulent Delta variant and the waning efficacy of the vaccine, messaging shifted to, “Get boosted and avoid hospitalization and death.”(See, for example, "Joint Statement from HHS Public Health and Medical Experts on COVID-19 Booster Shots"). Omicron led to a higher incidence of breakthrough infections, and the messaging shifted to, “Get boosted, avoid hospitalization and death, and return to social distancing and wear masks.” (See, for example, "The Possibility of COVID-19 after Vaccination: Breakthrough Infections").
Early adopters, some number of whom contracted COVID despite being vaccinated and boosted, are reevaluating the wisdom of attending live performances in enclosed spaces. This pull-back is most obvious in counties with the highest level of vaccinations, which are also counties with an older population. This explains lower ticket purchases in the most highly vaccinated counties.
Using our latest results, we show two simulations in Figure 3 that predict ticket sales from January 2021-June 2022. The key difference in the simulations is the assumption regarding future COVID case levels. The simulations use actual levels of COVID cases and vaccination rates from January 2021 to January 2022, when COVID cases exploded.
The cumulative impact over the first six months of 2022 is that ticket sales would be 13% higher if we incrementally return to June 2021 COVID case levels than if we only return to September 2021 levels.
The pandemic has changed our lives in many ways, but few if any industries have been more affected than the performing arts. Our model-free data visualizations and econometric model confirm the dramatic impact that COVID has had. The simulations calculate the impact of surging COVID cases and offer insights for performing arts leaders trying to manage supply and demand in the time of COVID. Quantifying the impact of the pandemic on this industry provides context for better understanding the future for live performances, the diminishing role that vaccinations and boosters play, and the increasing impact that COVID case rates have on demand for live, in-person performances.
This is our third publication examining changes in ticket sales as a result of changes in COVID indicators (see "Encouraging Signs from Three "What-If" Scenarios this Holiday Season" and "How Vaccination Rates May Impact Performing Arts Ticket Sales Through March 2022"). This latest update uses aggregated ticket data through December 31, 2021, provided by our partner TRG Arts. Transactions for each organization were summed up for each household census tract (HHCT) on a monthly basis. We then estimated a zero-inflated negative binomial model predicting the number of transactions each month in each HHCT that incorporates the following influences (see "Managing Supply and Demand for the Performing Arts in the Time of COVID" for complete details):
Figure 2 shows how the model fit actual data for organizations operating in counties with vaccination rates that ranged from 10-60% in June 2021 and 10-80% in September and December 2021. The results show how the effect of vaccination rates has evolved over time, strictly positive early on and at relatively low vaccination rates, diminishing positive effects at higher rates, and becoming negative more recently at very high rates. Figure 3 extrapolates the model results to predict future ticket purchases as vaccination rates continue to rise and COVID case rates peak in January and slowly or more rapidly abate.
As the statistician George Box is often quoted as saying, “All models are wrong, but some are useful.” Our model is surely wrong, especially when predicting sales levels outside the range of the data used, which peaks at an 86% vaccination rate and at case rates far below January 2022 levels. But we hope the results are useful. We will continue to update the model and share new results as new data become available.
Nattamai Kannan, Karthik Babu and Dhungana, Govinda and Voss, Glenn B., Managing Supply and Demand for the Performing Arts in the Time of COVID (November 19, 2021). SMU Cox School of Business Research Paper No. 21-14, Available at SSRN: "Managing Supply and Demand for the Performing Arts in the Time of COVID" or here.
A CONVERSATION WITH THE RESEARCHERS
When the COVID-19 virus began spreading across the U.S., researchers at SMU DataArts responded by integrating datasets and building a framework for predicting ticket purchasing demand. Continually refined for over a year, this framework takes into account ticketing purchases, census data, COVID cases, vaccine rates, restaurant employment, and arts ticket prices to help organizations across the nation predict demand for in-person ticket purchases. Get a behind-the-scenes look at how the model was developed and how early actual results compare with predictions.
Karthik Babu Nattamai Kannan, Ph.D., is an Assistant Professor of Information Technology and Operations Management at Cox School of Business, Southern Methodist University and the Donna Wilhelm Research Fellow at SMU DataArts. He studies how technological innovations are changing how people access the internet, consume digital entertainment and participate in e-commerce platforms. He also studies how retailers and art institutions can leverage mobile location data to improve their service operations. His research interests include location analytics, electronic/mobile commerce, and social media analytics. In his research, he uses empirical methods such as advanced econometrics, machine learning, field/natural experiments, etc., and optimization models to study large-scale datasets.